{"id":"W65872913","doi":"","title":"Identifying High Collision Locations Without Traffic Volume Data","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Collision; Computer science; Traffic volume; Volume (thermodynamics); Negative binomial distribution; Binomial distribution; Overdispersion; Simulation; Data mining; Transport engineering; Statistics; Engineering; Computer security; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001580155,0.0006161365,0.0004726041,0.006298237,0.0007095575,0.001332221,0.001278006,0.0005135598,0.002452445],"category_scores_gemma":[0.007781567,0.0004184326,0.000448949,0.004668436,0.0003881847,0.0008669383,0.001519155,0.0004665458,0.000635574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001759882,"about_ca_system_score_gemma":0.002795795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09726866,"about_ca_topic_score_gemma":0.1893435,"domain_scores_codex":[0.9977143,0.0003655049,0.0002056863,0.0004438167,0.0009711233,0.0002996706],"domain_scores_gemma":[0.9950773,0.00109575,0.001260805,0.0003823666,0.001954799,0.0002289661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002007729,0.0001308449,0.9292518,0.0002013514,0.000107235,0.0007357021,0.001043704,0.01170429,0.002471108,0.001403376,0.005483589,0.04726615],"study_design_scores_gemma":[0.00003796372,0.0002678632,0.8977548,0.0001544143,0.0001533568,0.0006578497,0.003809246,0.0785277,0.005895893,0.001588753,0.01104837,0.0001038011],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9356991,0.0002413302,0.0432335,0.0001300015,0.00006794312,0.0007013624,0.007915258,0.0009723047,0.01103916],"genre_scores_gemma":[0.9639058,0.0001537288,0.02771138,0.00004674881,0.000008959342,0.0002668181,0.005975622,0.00003930811,0.001891619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09726866,"threshold_uncertainty_score":0.193405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09699712958836344,"score_gpt":0.3772918317837832,"score_spread":0.2802947021954198,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}